Intercomparison of SSU temperature data records with Lidar, GPS RO, and MLS observations


  • Likun Wang,

    1. Cooperative Institute for Climate and Satellites/Earth System Science Interdisciplinary Center/University of Maryland, College Park, Maryland, USA
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  • Cheng-Zhi Zou

    1. NOAA/National Environmental Satellite, Data, and Information Services/Center for Satellite Applications and Research, College Park, Maryland, USA
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Corresponding author: L. Wang, 5825 University Research Court, RM 4001, College Park, MD 20740-3823, USA. (


[1] A consistent long-term stratospheric temperature observation is a crucial part for global change studies. In this study, we compare newly developed Stratospheric Sounding Unit (SSU) layer-averaged stratospheric temperatures with lidar, GPS Radio Occultation (RO), and Microwave Limb Sounder (MLS) stratospheric temperature profiles, each with unique error characteristics, spatial and temporal coverage, and observational principles. The vertical temperature profiles are converted into SSU-equivalent layer temperatures, and diurnal correction is applied to adjust the observations into an identical observational time. The comparison is carried out on pentad grids with a 2.5° latitude × 2.5° longitude resolution. Grid-by-grid comparison of SSU and MLS gives the mean differences between them from August 2004 to May 2006 of -0.041, 0.169, and -0.447 K with standard deviation of 1.180, 1.485, and 1.715 K for SSU channels 1–3, respectively. The correlation of GPS RO and SSU brightness temperature anomalies are 0.943, 0.877, and 0.699 from channels 1–3, respectively, and the correlation decreases with altitude (channels). SSU channel 3 brightness temperature anomalies are correlated with lidar observations with correlation coefficients of 0.839 at the Hohenpeissenberg Observatory in Germany and 0.725 at the Observatoire de Haute-Provence in France. Overall, the comparison results do not show that the newly developed SSU data set is significantly different from any of the three independent data sets based on known limitations and advantages of these data sets.